Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

Linear optimization software

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

Represent trained machine learning models as Pyomo optimization formulations

A Julia/JuMP-based Global Optimization Solver for Non-convex Programs

General optimization (LP, MIP, QP, continuous and discrete optimization etc.) using Python

A Julia interface to the Gurobi Optimizer

Efficient modeling interface for mathematical optimization in Python

Branch-and-Price-and-Cut in Julia

A JuMP-based Nonlinear Integer Program Solver

Derivative-Free Global Optimization Algorithm (C++, Python binding) - Continuous, Discrete, TSP, NLS, MINLP

A Julia interface to the CPLEX solver

A solver for mixed-integer convex optimization

Certifiable Outlier-Robust Geometric Perception

Julia interface to SCIP solver

Hands-on course about linear programming and mathematical optimization.

Humble 3D knapsack / bin packing solver

A Julia interface to the Coin-OR Branch and Cut solver (CBC)

An open-source parallel optimization solver for structured mixed-integer programming

BCP-MAPF – branch-and-cut-and-price for multi-agent path finding